Modeling heterogeneous risk-taking behavior in route choice: A stochastic dominance approach

This paper proposes a unified approach to modeling heterogonous risk-taking behavior in route choice based on the theory of stochastic dominance (SD). Specifically, the first-, second-, and third-order stochastic dominance (FSD, SSD, TSD) are respectively linked to insatiability, risk-aversion and ruin-aversion within the framework of utility maximization. The paths that may be selected by travelers of different risk-taking preferences can be obtained from the corresponding SD-admissible paths, which can be generated using general dynamic programming. This paper also analyzes the relationship between the SD-based approach and other route choice models that consider risk-taking behavior. These route choice models employ a variety of reliability indexes, which often make the problem of finding optimal paths intractable. We show that the optimal paths with respect to these reliability indexes often belong to one of the three SD-admissible path sets. This finding offers not only an interpretation of risk-taking behavior consistent with the SD theory for these route choice models, but also a unified and computationally viable solution approach through SD-admissible path sets, which are usually small and can be generated without having to enumerate all paths. A generic label-correcting algorithm is proposed to generate FSD-, SSD-, and TSD-admissible paths, and numerical experiments are conducted to test the algorithm and to verify the analytical results.     

Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities

The goal of a network design problem (NDP) is to make optimal decisions to achieve a certain objective such as minimizing total travel time or maximizing tolls collected in the network. A critical component to NDP is how travelers make their route choices. Researchers in transportation have adopted human decision theories to describe more accurate route choice behaviors. In this paper, we review the NDP with various route choice models: the random utility model (RUM), random regret-minimization (RRM) model, bounded rationality (BR), cumulative prospect theory (CPT), the fuzzy logic model (FLM) and dynamic learning models. Moreover, we identify challenges in applying behavioral route choice models to NDP and opportunities for future research.     

Assessing asymmetric response effect of behavioral intention to service quality in an integrated psychological decision-making process model of intercity bus passengers: a case of Taiwan

This study introduces the concept of loss aversion to consumer behavioral intention at the personal psychological level to develop an integrative structural equation model for analyzing traveler psychological decision making. In this model, the relationship between behavioral intention and service quality is a non-smooth function based on the theory of loss aversion. The expectation service quality in the SERVQUAL model proposed by Parasuraman, Zeithaml, and Berry (PZB) serves as a reference point. This model can be applied to analyze the effect of non-smooth response of behavioral intention to service quality in a traveler psychological decision-making process model. Intercity travel among cities in Taiwan is used as an empirical example. Data were gathered in cities in Taiwan via a questionnaire survey, and the model was tested using path analysis performed by LISREL. The empirical result shows that all causal relationships are statistically significant. Service quality loss influences repurchase intention more than does Service quality gain. Finally, this study concludes by discussing managerial implications and suggesting directions for future research.

跨境供应链网络海外仓的鲁棒性选址模型及算法

新经济形态下，全球政治、经济形势瞬息万变，多种人为和自然因素导致商业供应链的生存环境日益严峻. 跨境供应链网络作为典型的时效性和运营环境等都具有极大不确定性的复杂网络，研究其鲁棒性问题具有一定的理论意义和实际应用场景. 首先阐明了跨境供应链网络的内涵，认为海外仓的选址优化是控制网络鲁棒性的重要手段；随后基于最小最大后悔值构建了海外仓的鲁棒性选址模型，利用情景松弛算法对模型进行求解. 算例分析结果证明，所构建的海外仓鲁棒性选址模型及算法适用于跨境供应链网络情景.     

诱导信息下城市轨道交通乘客路径选择行为研究

为研究城市轨道交通诱导信息对乘客的影响，考虑信息提供时间、方式、更新频率及其与 路径属性之间的交互关系，提出融合诱导信息的城市轨道交通乘客路径选择行为决策框架。考 虑乘客在决策时表现出遵循混合决策规则的倾向差异和不同属性感知差异，建立改进的混合效用-后悔模型。将路径属性与诱导信息同时纳入调研情景设计，基于调研结果标定模型中关键参数，结果表明，基于混合决策规则的模型拟合效果最高(调整优度比达0.396)。进一步分析乘客对信息的偏好：相较于下载手机APP，乘客更喜欢社交媒体推送的形式接收诱导信息；引入信息推送频率时间价值，并对比得出，女性、中老年及非通勤乘客更倾向于接受更高频率的诱导信息推送服务；分析各属性弹性值，表明诱导信息提供方式和更新频率应作为管理者的辅助手段，在特定场景下(例如高峰拥堵和突发事故)诱导信息将会发挥更大的作用。研究揭示了诱导信息提供方式如何影响轨道交通乘客的路径决策机理，有助于设计更加精准和高效的诱导信息策略辅助客流组织管理。     

考虑出行者不同理性程度的拥堵交通流分配方法

为研究出行者感知偏好对交通分配结果的影响,本文构建了微观路径选择模型,提出拥堵条件下受路段通行能力限制的交通分配算法。引入出行者决策过程中的后悔和无差别化阈值,考虑出行时间和排队时间的心理感知差异,构建不同理性程度下的路径选择概率模型。在集计水平上,考虑当前路段及其上下游路段通行能力限制、路段车辆空间排队和溢出,提出路段车流量流入、流出的修正方法。采用增量加载分配方法,研究路段车辆的消散特性,再现了从个体路径决策到宏观路网状态的演化过程。基于Nguyen-Dupuis仿真网络,比较不同算法下各路段的拥堵车辆和各路段车辆流入、流出情况。结果表明：出行者个人偏好感知会显著影响拥堵路段的成本函数,是出行者路径选择的关键因素,但是出行者个人偏好对非拥堵路段的车辆流入、流出影响较小;考虑个体偏好的交通分配方法能降低路网的平均饱和度。本文提出的考虑有限理性的拥堵交通分配方法可应用于拥堵路网的交通诱导,有利于促进道路资源的合理利用。     

规避隧道施工风险的措施探讨

随着我国公路、铁路隧道大规模开工建设和为了更好地保护环境、少占土地,隧道的规模（长度和断面）越来越大,技术质量标准越来越高,同时也加大了隧道施工风险。对隧道施工风险进行分析,既可以提高规避隧道施工风险的能力,又能保证隧道施工又好又快地进行。     

考虑出行者损失厌恶的后悔随机用户均衡模型

在经典随机后悔最小化模型的基础上,通过引入出行者获益损失的不对称偏好,建立了考虑出行者损失厌恶的属性水平的后悔函数及基于Logit形式的随机用户均衡模型.在提出的属性水平的后悔函数中,等尺度的获益和损失所产生的欣喜和后悔的差异,受后悔欣喜偏好参数和出行者损失厌恶共同影响.此外,与上述随机用户均衡模型等价的变分不等式问题被给出,并用相继平均法求解.最后,用1个算例网络来验证所提出模型的合理性和算法的可行性.结果表明,出行者的损失厌恶对其路径选择行为具有较大的影响,并且随着损失厌恶程度的增大,出行者更倾向于选择最短路径.     

A State-of-the-Art Review: Developments in Utility Theory,Prospect Theory and Regret Theory to Investigate Travellers' Behaviour in Situations Involving Travel Time Uncertainty

Abstract

Despite the wide use of utility theory to model travellers' behaviour, the interest in non-expected utility theories has increased due to their potential to capture more realistic behaviour. A main question raised is whether travellers are better described as utility maximizers or should be qualified differently.

This paper presents a literature review on the use of expected utility theory (EUT), prospect theory (PT) and regret theory (RT) to model travellers' behaviour. Gaps in the literature are identified and a discussion about advantages and disadvantages of each theory is presented. A case study illustrates the differences between the theories.

Under certain conditions, PT and RT restrict themselves to EUT. Their added value, however, is the possibility of capturing loss aversion, risk aversion and risk-seeking (PT) and regret aversion (RT). On the practical level, the use of EUT is well established, while contributions of PT and RT are marginal. On the theoretical level, however, RT seems to be (marginally) more suitable to model travellers' behaviour, while EUT and PT are equally suitable. This suggests that the large use of EUT is highly influenced by its very tractable framework. We do not claim the superiority of any theory, but propose to compare them through a systematic review.     

考虑异质性的经典随机后悔最小化模型改进研究

传统基于后悔理论的交通行为模型对异质性考虑不充分，对真实选择行为的解释存在不足.本文利用韦伯比率，考虑出行者对各方式属性变量感知的异质性，对经典随机后悔最小化模型进行改进，分别建立基于随机效用最大化，经典随机后悔最小化和改进经典随机后悔最小化的选择模型；以网约车选择行为为例进行实证研究，验证改进模型效果.结果表明：3种模型参数标定结果具有一致性，改进模型的拟合优度(0.271)和命中率(75.8%)相较于另外两个模型均较优；改进模型能更好地描述多维属性决策过程中的半补偿原则和折中效应，可以提高模型对真实选择行为的解释能力.